2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)最新文献_第3页

Research on Bidirectional DC/DC Converter Based on Passive Control 基于无源控制的双向DC/DC变换器研究

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787627

X. Tang, Juan Li, Qihong Chen, Liyan Zhang

引用次数: 2

Design of A Double-blockchain Structured Carbon Emission Trading Scheme with Reputation 具有信誉的双区块链结构碳排放交易方案设计

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787720

Xufeng Liang, Y. Du, Xu Wang, Yuliang Zeng

引用次数: 15

On Theoretical Analysis of Single Hidden Layer Feedforward Neural Networks with Relu Activations 带Relu激活的单隐层前馈神经网络的理论分析

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787645

Guorui Shen, Ye Yuan

{"title":"On Theoretical Analysis of Single Hidden Layer Feedforward Neural Networks with Relu Activations","authors":"Guorui Shen, Ye Yuan","doi":"10.1109/YAC.2019.8787645","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787645","url":null,"abstract":"During past decades, extreme learning machine has acquired a lot of popularity due to its fast training speed and easy-implementation. Though extreme learning machine has been proved valid when using an infinitely differentiable function like sigmoid as activation, existed extreme learning machine theory pays a little attention to consider non-differentiable function as activation. However, other non-differentiable activation function, rectifier linear unit (Relu) in particular, has been demonstrated to enable better training of deep neural networks, compared to previously wide-used sigmoid activation. And today Relu is the most popular choice for deep neural networks. Therefore in this note, we consider extreme learning machine that adopts non-smooth function as activation, proposing that a Relu activated single hidden layer feedforward neural network (SLFN) is capable of fitting given training data points with zero error under the condition that sufficient hidden neurons are provided at the hidden layer. The proof relies on a slightly different assumption from the original one but remains easy to satisfy. Besides, we also found that the squared fitting error function is monotonically non-increasing with respect to the number of hidden nodes, which in turn means a much wider SLFN owns much expressive capacity.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"40 1","pages":"706-709"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91519119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Design of Automatically Conveying Materials Control System Based on TIA portal Configuration Software 基于TIA门户组态软件的自动输送物料控制系统设计

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787638

Kou Zuxing, Zhang Wei, Zhang Libao

引用次数: 1

Container number localization based on SSD algorithm 基于SSD算法的集装箱编号定位

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787699

Bingqian Ding, Jianming Guo, Junhong Hu

引用次数: 0

Gaussian Kernel Least Mean Square Algorithm With Improved Novelty Criterion 改进新颖性准则的高斯核最小均方算法

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787677

Fuping Wang, Yixin Su, Zhiwen Leng, Yue Qi

引用次数: 0

Research on Digital Marketing Strategies of Fast Fashion Clothing Brands Based on Big Data 基于大数据的快时尚服装品牌数字化营销策略研究

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-06 DOI: 10.1109/YAC.2019.8787647

Y. Liu, Taoye Zhang

{"title":"Research on Digital Marketing Strategies of Fast Fashion Clothing Brands Based on Big Data","authors":"Y. Liu, Taoye Zhang","doi":"10.1109/YAC.2019.8787647","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787647","url":null,"abstract":"With the rapid development of fast fashion brands in today's market, a large number of international fast fashion brands have entered the clothing market in China, which has enabled Chinese consumers to experience something entirely new by means of digital marketing strategies, thus making China's fast fashion clothing brands in an inferior position. ZARA, HM, UNIQLO and other leading fast fashion brands have developed rapidly in China, which is inseparable from their marketing strategies. Therefore, this paper firstly summarizes fast fashion brand, big data and digital marketing, collects data released by authoritative agencies, and analyses the necessity of fast fashion brands entering the digital stage. Secondly, the successful cases of digital marketing of representative fast fashion brands are studied in detail. Finally, by summing up the enlightening conclusions of the study, this paper puts forward some suggestions on the development strategy of digital marketing for fast fashion brands in China, and creates some marketing strategies suitable for the development of Chinese enterprises themselves.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"101 1","pages":"552-556"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84762055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Hough Line Detection Based Glass Surface Defects Recognition Algorithm 基于霍夫线检测的玻璃表面缺陷识别算法

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-01 DOI: 10.1109/YAC.2019.8787639

Zifan Li, Hua Zhou

引用次数: 1

Research on Structural Measuring Point Optimization Method Based on Improved Particle Swarm Optimization 基于改进粒子群算法的结构测点优化方法研究

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-01 DOI: 10.1109/YAC.2019.8787661

Guan Lu, Tongyang Feng, Xinyong Ma, Yiming Xu

{"title":"Research on Structural Measuring Point Optimization Method Based on Improved Particle Swarm Optimization","authors":"Guan Lu, Tongyang Feng, Xinyong Ma, Yiming Xu","doi":"10.1109/YAC.2019.8787661","DOIUrl":"https://doi.org/10.1109/YAC.2019.8787661","url":null,"abstract":"Aiming at the problem of too many measuring points and difficult optimization in the process of I-beam structure stress state analysis, an improved particle swarm optimization algorithm based on simulated annealing and genetic algorithm is proposed, which considers the identification error of stress state and the optimization of measuring points comprehensively to screen the measuring points. Firstly, the initialization, selection, crossover and mutation of genetic algorithm are integrated into particle swarm optimization; secondly, the idea of simulated annealing is introduced into the mutation part. The improved particle swarm optimization algorithm improves the premature and poor local optimization of the standard particle swarm optimization algorithm. Compared with the standard particle swarm optimization, the improved particle swarm optimization has better stability, stronger anti-premature ability and 60% higher accuracy. The simulation results of measuring point selection and stress state identification of I-beam show that the improved particle swarm optimization algorithm has high efficiency in the process of point selection of stress state identification. The error of force state identification to select points is less than 3%. In engineering application, it provides a better method for stress state identification.","PeriodicalId":6669,"journal":{"name":"2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"5 1","pages":"335-341"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78392982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Planar formation control using tensegrity structures and experiments 平面编队控制的张拉整体结构和实验

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2019-06-01 DOI: 10.1109/YAC.2019.8787707

Qingkai Yang, Yunlong Pan, Bo Zhou, H. Fang

引用次数: 0